World markets have seen a tremendous increase in demand for electronic devices that employ analog or radio-frequency (RF) circuitry such as cellular phones, wireless LAN and WiFi components, oscilloscopes, and navigation systems. There is a corresponding demand for analog or RF components such as mixers, amplifiers, analog switches, converters, and transceivers. This increase in demand is forcing the industry to find cost effective ways to manufacture these devices. Device integration has been used to make manufacturing more efficient by reducing manufacturing and material costs, while at the same time improving reliability. While the fabrication costs of integrated devices are becoming less expensive, the cost of testing such devices remains high. Test costs for a given production device include a share of the cost of any test instrumentation required, as well as the time required for testing using that instrumentation. Pressure to keep test costs low will increase with the integration of devices into consumer applications, which must have low overall cost.
The current high cost of analog or RF device testing is caused by a lack of good test methods. Unlike digital device testing, where structural test methods are used for device testing, most analog or RF device testing applies lengthy functional tests requiring expensive equipment. For example, time consuming and expensive functional tests include adjacent channel power measurement, channel selectivity, bit error rate (BER), and error vector magnitude (EVM). Each functional test checks compliance of the resulting performance metric with the corresponding performance specification for the device design. Furthermore, because functional tests often attempt to recreate the actual working environment of the device to measure performance metrics, simultaneous testing of multiple metrics can be difficult in functional testing protocols. Many current functional metric tests must run sequentially and/or use expensive equipment, which incurs very high costs. Also, the coverage that these metric tests provide is not well understood, which results in possibly redundant tests being included in the test flow. This increases costs and adds redundancy.
FIG. 1 shows a flow diagram of a conventional method for testing for the effect of process parameter variations on the performance metrics of a production device. The conventional test method does not specifically target defects. Instead, it directly measures the performance metrics of each production device and compares them to respective performance specifications of the device design to make a decision as to whether the production device is good or bad. In FIG. 1, in block 102, a first stimulus is applied to the production device. In block 104, the response of the production device to the first stimulus is measured. In block 106, a first performance metric for the production device is determined from the response measured in block 104. In block 106, the performance metric is typically determined by the automatic tester also used to perform above-described blocks 102 and 104. Alternatively, the determination is made by other known means. In block 108, test is performed to determine whether the first performance metric meets a first performance specification. If the test result is NO, the production device is classified as bad (block 110). If the test result in block 108 is YES, the process just described is repeated using a different stimulus to test a different performance metric. In all, N stimuli are sequentially applied to each production device and N responses are measured. A production device for which all N performance metrics tested meet respective performance specifications is classified as good and is released for sale (block 112). Alternatively, all N performance metrics may be determined before the compliance of the performance metrics with respective performance specifications is determined.
Other methods have been proposed that attempt to use a single measurement or a small set of measurements to derive a larger set of performance metrics for the production device. In these methods, alternative (non-functional) measurements of the production device are taken. The alternative measurements are meant to provide a signature for the production device. The signature is then regressed over the conventional performance metrics. The alternative measurements are designed to give required resolution in the regression for the targeted performance metrics. However, such prior art methods may miss some of the behaviors that may be relevant for detecting device defects. Also, because some prior art methods have used linear relationships to derive performance metrics, they have been inherently limited to production devices whose behavior is capable of being modeled using linear modeling. Also, prior art methods that use stimulus-response measurements must be carefully designed with full knowledge of the tests that will be used select a tuned stimulus, and are not readily adaptable to additional measurements of performance metrics.